Appsierra runs quality engineering, software development and AI engineering as senior-led pods on a single contract. Agents handle the volume, senior engineers sign off, and our own evaluation platform gates every output before it ships. Open a practice below to see what the pod delivers, the stack it works in and the metric it is held to.
PRACTICESTACKHELD TOGROUP
WHAT THE POD DELIVERS
Agents that do real work inside your systems: scoped tool access, deterministic fallbacks, human-in-the-loop approvals and an audit trail per action. We ship with an eval suite so you can see task success and regression before an agent touches production data.
Retrieval design, context engineering, prompt and model evaluation, fine-tuning where it pays, and the ops layer around it: caching, routing, cost ceilings and observability. Model-agnostic by design so you are never re-platformed by a vendor price change.
Our moat. Evaluation sets built from your domain, bias and safety checks, adversarial red-teaming, PII redaction and pipeline gates that block a release when a score drops. This is the same platform that vets our own engineers.
Flagship practice. AI-generated tests reviewed by senior SDETs, risk-based regression, self-healing selectors and release gates that actually hold. Includes testing of AI features, not just traditional paths.
Rebuild or rescue an automation suite the team stopped trusting: triage the flake, restructure the layers, wire it into CI with parallel execution and clear ownership. Manual regression cycles collapse into an automated run.
Full-stack delivery by one pod: frontend, APIs and data, tested end to end as it is built. The same senior engineer who wrote it is on the call when something breaks.
Dedicated frontend deploy engineers who own the path from commit to production: preview environments per pull request, edge caching, bundle budgets, feature flags, instant rollback and Core Web Vitals held to a number.
CI/CD you can reason about, infrastructure as code, environment parity, observability and on-call runbooks. We leave the platform documented and handed over, not held hostage.
Pipelines with tests, contracts and lineage; warehouse modelling that analysts can read; and the semantic layer your AI features query instead of guessing.
Application security testing, compliance evidence, penetration work and AI supply-chain review — including what your models, prompts and agents can reach.
Describe the outcome you need rather than a specification. A senior engineer reads it and replies with a scope, a metric and the smallest way to prove it.
Service FAQs
How do you price a pod?
Pricing is per pod and per role seniority, quoted after a free 30-minute call once the scope is clear. Most engagements start with a small paid pilot tied to one success metric, so you see the work before committing to a larger contract.
How fast can a pod start?
Pre-vetted pods are typically productive in about 7 days, because every engineer is already evaluated through our own platform before they reach you. A GCC build is a different shape: stood up, run and transferred to you inside six months.
Can you work inside our existing tools and process?
Yes. Pods work in your repos, your CI, your ticketing and your review process rather than asking you to adopt ours. The engagement is measured on your metric, not on our internal reporting.
Who is accountable if the work slips?
We are. A pod is an outcome-owned engagement with a named senior engineer accountable for delivery, not a pool of contractors you manage yourself. That accountability is the difference between this and a talent marketplace.
Do we own the code and IP?
You do, from day one. NDA and MSA are signed before any access is granted, and nothing is subcontracted without your written consent.
Vetted pods, productive in 7 days
Senior-reviewed pods · live in ~7 days · cancel anytime
Tell us what you need to build, test, scale or hire for — QA, software, AI/LLM engineering or a full pod. A senior engineer reviews it and sends a short, honest read, plus a low-risk way to start.
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